A verified realization of a Dempster–Shafer based fault tree analysis

Fault tree analysis is a method to determine the likelihood of a system attaining an undesirable state based on the information about its lower level parts. However, conventional approaches cannot process imprecise or incomplete data. There are a number of ways to solve this problem. In this paper, we will consider the one that is based on the Dempster–Shafer theory. The major advantage of the techniques proposed here is the use of verified methods (in particular, interval analysis) to handle Dempster–Shafer structures in an efficient and consistent way. First, we concentrate on DSI (Dempster–Shafer with intervals), a recently developed tool. It is written in MATLAB and serves as a basis for a new add-on for Dempster–Shafer based fault tree analysis. This new add-on will be described in detail in the second part of our paper. Here, we propagate experts’ statements with uncertainties through fault trees, using mixing based on arithmetic averaging. Furthermore, we introduce an implementation of the interval scale based algorithm for estimating system reliability, extended by new input distributions.     

An innovative method based on satellite image analysis to check fault in a PV system lead–acid battery

Batteries are an important part in photovoltaic systems. They ensure reliability and good-operation of the overall PV system. In this paper, we proposed a method based on the estimation of the solar radiation to check the faults that occur in the lead–acid batteries. At first, the GISTEL (Gisement solaire par télédetection: Solar Radiation by Teledetection) model is chosen as a satellite image approach to estimate the hourly global solar radiation. Secondly, the estimated data are selected as input to check the faults of the lead–acid battery. A simple and effective method is developed to detect the internal resistance effect as well as the overcharging problem during the charging and discharging cycles. The experimental results show the easiness of the proposed method that possesses a good accuracy.     

Dynamic modeling of SOFC based on a T–S fuzzy model

The operating temperature and voltage are the key parameters affecting the performance of Solid Oxide Fuel Cell (SOFC). In this article a Takagi–Sugeno (T–S) fuzzy model is proposed to describe the nonlinear temperature and voltage dynamic properties of the SOFC system. During the process of modeling, a Fuzzy Clustering Means (FCM) method is used to determine the nonlinear antecedent parameters, and the linear consequent parameters are identified by a recursive least squares algorithm. The validity and accuracy of modeling are tested by simulations. The simulation results show that it is feasible to establish the dynamic model of SOFC by using the T–S fuzzy identification method.     

New stability analysis of T–S fuzzy system with robust approach

Based on the robustness criterion, this paper provides a new way to deal with the stability of T–S fuzzy system. A sufficient criterion is proposed to guarantee quadratic stability of the fuzzy model globally. The existence well-known problem of the common P for a set of Lyapunov equations in fuzzy control system design can be avoided here. An illustrative example is given to show the effectiveness of the proposed method.     

T–S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm

This paper proposes a novel approach for identification of Takagi–Sugeno (T–S) fuzzy model, which is based on a new fuzzy c-regression model (FCRM) clustering algorithm. The clustering prototype in fuzzy space partition is hyper-plane, so FCRM clustering technique is more suitable to be applied in premise parameters identification of T–S fuzzy model. A new FCRM clustering algorithm (NFCRMA) is presented, which is deduced from the fuzzy clustering objective function of FCRM with Lagrange multiplier rule, possessing integrative and concise structure. The proposed approach consists mainly of two steps: premise parameter identification and consequent parameter identification. The NFCRMA is utilized to partition the input–output data and identify the premise parameters, which can discover the real structure of the training data; on the other hand, orthogonal least square is exploited to identify the consequent parameters. Finally, some examples are given to verify the validity of the proposed modeling approach, and the results show the new approach is very efficient and of high accuracy.     

Dissipative control for Markov jump non-linear stochastic systems based on T–S fuzzy model

The dissipative analysis and control problems for a class of Markov jump non-linear stochastic systems (MJNSSs) are investigated. A sufficient condition for the dissipativity of MJNSSs is given in terms of coupled non-linear Hamilton–Jacobi inequalities (HJIs). Generally, it is difficult to solve the coupled HJIs. In this paper, based on T–S fuzzy model, the dissipative analysis and controller design for MJNSSs is proposed via solving a set of linear matrix inequalities (LMIs) instead of HJIs. Finally, a numerical example is presented to show the effectiveness of the proposed method.     

A new gear fault feature extraction method based on hybrid time–frequency analysis

Gear is one of the popular and important components in the rotary machinery transmission. Vibration monitoring is the common way to take gear feature extraction and fault diagnosis. The gear vibration signal collected in the running time often reflects the characteristics such as non-Gaussian and nonlinear, which is difficult in time domain or frequency domain analysis. This paper proposed a novel gear fault feature extraction method based on hybrid time–frequency analysis. This method combined the Mexican hat wavelet filter de-noise method and the auto term window method at the first time. This method can not only de-noise noise jamming in raw vibration signal, but also extract gear fault features effectively. The final experimental analysis proved the feasibility and the availability of this new method.     

Multi-sensor fusion approach with fault detection and exclusion based on the Kullback–Leibler Divergence: Application on collaborative multi-robot system

This paper presents a multi-sensor fusion strategy able to detect the spurious sensors data that must be eliminated from the fusion procedure. The used estimator is the informational form of the Kalman Filter (KF) namely Information Filter (IF). In order to detect the erroneous sensors measurements, the Kullback–Leibler Divergence (KLD) between the a priori and a posteriori distributions of the IF is computed. It is generated from two tests: One acts on the means and the other deals with the covariance matrices. Optimal thresholding method based on a Kullback–Leibler Criterion (KLC) is developed and discussed in order to replace classical approaches that fix heuristically the false alarm probability.Multi-robot systems became one of the major fields of study in the indoor environment where the environmental monitoring and the response to crisis must be ensured. Consequently, the robots required to know precisely their positions and orientations in order to successfully perform their mission. Fault detection and exclusion (FDE) play a crucial role in enhancing the integrity of localization of the multi-robot team. The main contributions of this paper are: - developing a new method of sensors data fusion that tackle the erroneous data issues, - developing a Kullback–Leibler based criterion for the threshold optimization, - Validation with real experimental data from a group of robots.     

Efficient application scheduling in mobile cloud computing based on MAX–MIN ant system

Combining the advantages of mobile computing and cloud computing, Mobile Cloud Computing (MCC) greatly enriches the types of applications on mobile devices and enhances the quality of service of the applications. Under various circumstances, researchers have put forward several MCC architectures. However, it still remains a challenging task of how to design a reasonable mobile cloud model with efficient application processing structure for some particular environment. This paper firstly presents a Hybrid Local Mobile Cloud Model (HLMCM) with detailed application scheduling structure. Secondly, a scheduling algorithm for HLMCM based on MAX–MIN Ant System is put forward. Finally, the effectiveness and suitability of our proposed algorithms are evaluated through a series of simulation experiments.     

T–S fuzzy model adopted SLAM algorithm with linear programming based data association for mobile robots

This paper describes a Takagi–Sugeno (T–S) fuzzy model adopted solution to the simultaneous localization and mapping (SLAM) problem with two-sensor data association (TSDA) method. Nonlinear process model and observation model are formulated as pseudolinear models and rewritten with a composite model whose local models are linear according to T–S fuzzy model. Combination of these local state estimates results in global state estimate. This paper introduces an extended TSDA (ETSDA) method for the SLAM problem in mobile robot navigation based on an interior point linear programming (LP) approach. Simulation results are given to demonstrate that the ETSDA method has low computational complexity and it is more accurate than the existing single-scan joint probabilistic data association method. The above system is implemented and simulated with Matlab to claim that the proposed method yet finds a better solution to the SLAM problem than the conventional extended Kalman filter–SLAM algorithm.     

Evolving Takagi–Sugeno fuzzy model based on switching to neighboring models

In this paper, we propose a new online identification approach for evolving Takagi–Sugeno (TS) fuzzy models. Here, for a TS model, a certain number of models as neighboring models are defined and then the TS model switches to one of them at each stage of evolving. We define neighboring models for an in-progress (current) TS model as its fairly evolved versions, which are different with it just in two fuzzy rules. To generate neighboring models for the current model, we apply specially designed split and merge operations. By each split operation, a fuzzy rule is replaced with two rules; while by each merge operation, two fuzzy rules combine to one rule. Among neighboring models, the one with the minimum sum of squared errors – on certain time intervals – replaces the current model.To reduce the computational load of the proposed evolving TS model, straightforward relations between outputs of neighboring models and that of current model are established. Also, to reduce the number of rules, we define and use first-order TS fuzzy models whose generated local linear models can be localized in flexible fuzzy subspaces. To demonstrate the improved performance of the proposed identification approach, the efficiency of the evolving TS model is studied in prediction of monthly sunspot number and forecast of daily electrical power consumption. The prediction and modeling results are compared with that of some important existing evolving fuzzy systems.     

XDist： an effective XML keyword search system with re-ranking model based on keyword distribution

Keyword search enables web users to easily access XML data without understanding the complex data schemas. However, the native ambiguity of keyword search makes it arduous to select qualified relevant results matching keywords. To solve this problem, researchers have made much effort on establishing ranking models distinguishing relevant and irrelevant passages, such as the highly cited TF＊IDF and BM25. However, these statistic based ranking methods mostly consider term frequency, inverse document frequency and length as ranking factors, ignoring the distribution and connection information between different keywords. Hence, these widely used ranking methods are powerless on recognizing irrelevant results when they are with high term frequency, indicating a performance limitation. In this paper, a new searching system XDist is accordingly proposed to attack the problems aforementioned. In XDist, we firstly use the semantic query model maximal lowest common ancestor （MAXLCA） to recognize the returned results of a given query, and then these candidate results are ranked by BM25. Especially, XDist re-ranks the top several results by a combined distribution measurement （CDM） which considers four measure criterions： term proximity, intersection of keyword classes, degree of integration among keywords and quantity variance of keywords. The weights of the four measures in CDM are trained by a listwise learning to optimize method. The experimental results on the evaluation platform of INEX show that the re-ranking method CDM can effectively improve the performance of the baseline BM25 by 22% under iP[0.01] and 18% under MAiP. Also the semantic model MAXLCA and the search engine XDist perform the best in their respective related fields.     

Multiple classifier system for EEG signal classification with application to brain–computer interfaces

In this paper, we demonstrate the use of a multiple classifier system for classification of electroencephalogram (EEG) signals. The main purpose of this paper is to apply several approaches to classify motor imageries originating from the brain in a more robust manner. For this study, dataset II from BCI competition III was used. To extract features from the brain signal, discrete wavelet transform decomposition was used. Then, several classic classifiers were implemented to be utilized in the multiple classifier system, which outperforms the reported results of other proposed methods on the dataset. Also, a variety of classifier combination methods along with genetic algorithm feature selection were evaluated and compared in order to diminish classification error. Our results suggest that an ensemble system can be employed to boost EEG classification accuracy.     

Design of a modified repetitive-control system based on a continuous–discrete 2D model

This paper concerns the problem of designing a modified repetitive-control system for a class of strictly proper plants. Repetitive control involves two types of actions: control and learning; but the insertion of a low-pass filter in a modified repetitive controller, which is employed to guarantee the stability of the system, mixes the two actions together. In this paper, a continuous–discrete two-dimensional model is first constructed. Next, the continuity of repetitive control and Lyapunov stability theory are applied to the model to establish two linear-matrix-inequality (LMI) based sufficient stability conditions, one for the design of the cutoff angular frequency and one for the design of the feedback gains. The features of these conditions are exploited to develop an iterative algorithm that searches for the best combination of the maximum cutoff angular frequency of the low-pass filter and the feedback gains. A numerical example illustrates the design procedure and demonstrates the validity of the method.     

Finite-time stabilisation for discrete-time T–S fuzzy model system with channel fading and two types of parametric uncertainty

In this paper, the problem of finite-time stabilisation is firstly studied for the Takagi–Sugeno (T–S) fuzzy model system with channel fading and parameter uncertainty. Two theorems are given for the cases with different types of uncertainty. The sufficient conditions in the form of the linear matrix inequalities are derived such that the stabilisation of the closed-loop system is guaranteed. At last, some illustrative examples are employed to demonstrate the efficiency of the results.     

A new evolving compact optimised Takagi–Sugeno fuzzy model and its application to nonlinear system identification

A new encoding scheme is presented for a fuzzy-based nonlinear system identification methodology, using the subtractive clustering and non-dominated sorting genetic algorithm. The proposed method consists of two parts. The first part is related to the selection of most relevant or influencing inputs to the system and the second one is related to the tuning of fuzzy rules and parameters of the membership functions. The main purpose of the proposed scheme is to reduce the complexity and increase the accuracy of the model. In particular, three objectives are considered in the process of optimisation, namely, the number of inputs, number of rules and the root mean square of the modelling error. The performance of the developed method is validated by identifying the Box–Jenkins nonlinear benchmark system, and to the modelling of the forward and inverse dynamic behaviours of a magneto-rheological (MR) damper. The latter is also a challenging problem due to the inherent hysteretic and highly nonlinear dynamics of the MR damper. It is shown that the developed evolving Takagi–Sugeno (T–S) fuzzy model can identify and grasp the nonlinear dynamics of both systems very well, while a small number of inputs and fuzzy rules are required for this purpose.     

Exponential sampled-data control for T–S fuzzy systems: application to Chua's circuit

ABSTRACT

This study deals with the chaotic phenomenon of nonlinear Chua's circuit for power generator systems. Takagi–Sugeno (T–S) fuzzy model of a nonlinear system is established. By constructing a suitable Lyapunov functional, exponential stability conditions are obtained for fuzzy systems. Based on the sampled-data control theory, extreme sensitivity is visualised in the state trajectory depending on the initial conditions and sampled-data fuzzy controllers are designed in the form of linear matrix inequality (LMI). Finally, some numerical simulation results are shown that the sampled-data fuzzy control system adopts a well-designed methodology.     

Automatically controlled pan–tilt smart camera with FPGA based image analysis system dedicated to real-time tracking of a moving object

This paper presents an intelligent, automatically controlled camera based on visual feedback. The camera housing contains actuators that change the orientation of the camera – enabling a full rotation around the vertical axis (pan) and 90° around the horizontal axis (tilt). A system for acquisition, processing image analysis and a camera driver are implemented in the FPGA Xilinx Spartan-6 device. An original, innovative reconfigurable system architecture has been developed. The FPGA device is connected directly to the eight independently operated SRAM memory banks. A prototype device has been constructed with a real-time tracking algorithm, enabling an automatically control of the position of the camera. The device has been tested indoors and outdoors. The camera is able to keep a tracked object close to the center of its field of view. The power consumption of the control system is 2 W. A reconfigurable part reaches the computing performance of 3200 MOPS.     

Synchronisation analysis for stochastic T–S fuzzy complex networks with coupling delay

In this paper, we investigated synchronisation problem for stochastic Takagi–Sugeno (T-S) fuzzy complex networks model with discrete and distributed time delays. By constructing a new Lyapunov functional and employing Kronecker product, we developed delay-dependent synchronisation criterions. By applying stochastic analysis techniques, we derive starting conditions for synchronisation complex networks of the addressed with mixed time-varying delays and stochastic disturbances are achieved. A numerical examples are provided to demonstrate the effectiveness and usefulness of the proposed results.